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WalkthroughThis pull request introduces multiple new GitHub Actions workflows for automating the deployment of a static site generated by Changes
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Actionable comments posted: 20
🧹 Outside diff range and nitpick comments (27)
run-checks.sh (3)
3-4: Add error handling for pytest executionThe pytest execution should be checked for success before proceeding to coverage reporting.
# Run pytest with coverage -uv run pytest --cov=py_hamt tests/ +if ! uv run pytest --cov=py_hamt tests/; then + echo "Tests failed! Fixing tests should be the first priority." + exit 1 +fi
6-7: Consider a more gradual approach to coverage requirementsWhile 100% coverage is an admirable goal, it might be too strict initially and could discourage contributions. Consider:
- Starting with a lower threshold (e.g., 80%)
- Gradually increasing it as the codebase matures
- Using coverage configurations file instead of CLI arguments
# Check coverage -uv run coverage report --fail-under=100 --show-missing +# Create a .coveragerc file with: +# [report] +# fail_under = 80 +# show_missing = True +uv run coverage report
9-13: Add error handling for linting stepsThe linting and formatting steps should be checked for success and provide meaningful error messages.
# Check linting with ruff -uv run ruff check +if ! uv run ruff check; then + echo "Linting failed! Please fix the issues before proceeding." + exit 1 +fi # Auto format with ruff -uv run ruff format +if ! uv run ruff format; then + echo "Formatting failed! This is unexpected - please check ruff installation." + exit 1 +fi + +echo "All checks passed successfully! ✨".github/workflows/run-checks.yaml (1)
15-20: Add dependency caching to improve workflow performance.Consider adding caching for UV dependencies to speed up the workflow.
- name: Install uv uses: astral-sh/setup-uv@v3 + - name: Cache UV dependencies + uses: actions/cache@v3 + with: + path: .venv + key: ${{ runner.os }}-uv-${{ hashFiles('**/requirements.txt') }} + restore-keys: | + ${{ runner.os }}-uv- - name: Create project environment run: uv sync.github/workflows/pages-main.yaml (4)
5-8: Remove redundant permissions configuration.The permissions are defined both at the workflow level (lines 5-8) and job level (lines 14-17). Since they are identical, you can remove the job-level permissions to reduce redundancy.
permissions: contents: read pages: write id-token: write jobs: build: environment: name: github-pages url: ${{ steps.deployment.outputs.page_url }} - permissions: - contents: read - pages: write - id-token: write runs-on: ubuntu-latestAlso applies to: 14-17
24-28: Add caching for Python dependencies.Consider adding a caching step for uv dependencies to improve workflow execution time.
- name: Install uv uses: astral-sh/setup-uv@v3 + - name: Cache dependencies + uses: actions/cache@v4 + with: + path: .venv + key: ${{ runner.os }}-uv-${{ hashFiles('requirements.txt') }} - name: Create project environment run: uv sync
28-29: Enhance pdoc documentation generation.Consider adding additional pdoc options to improve the generated documentation:
- Add
--docformat googlefor better docstring parsing- Add
--show-sourceto include source code in documentation- name: Build with pdoc - run: uv run pdoc py_hamt -o ./_site + run: uv run pdoc py_hamt -o ./_site --docformat google --show-source
28-32: Add documentation verification step.Consider adding a step to verify the generated documentation before uploading:
- name: Build with pdoc run: uv run pdoc py_hamt -o ./_site + - name: Verify documentation + run: | + if [ ! -f "_site/index.html" ]; then + echo "Documentation generation failed - index.html not found" + exit 1 + fi - name: Upload artifact.github/workflows/pages-preview.yaml (2)
3-4: Add path and event type filters to optimize workflow triggers.Consider adding path filters to run only when documentation-related files change, and specify pull request types to avoid unnecessary runs.
name: Deploy static site generated by pdoc to GitHub Pages preview environment -on: pull_request +on: + pull_request: + types: [opened, synchronize, reopened] + paths: + - 'py_hamt/**/*.py' + - 'docs/**' + - '.github/workflows/pages-preview.yaml'
5-8: Remove duplicate permissions definition.The permissions are defined both at the workflow and job level. Since this workflow has only one job, the workflow-level permissions are sufficient.
permissions: contents: read pages: write id-token: write jobs: build: environment: name: "Pages Preview" url: ${{ steps.deployment.outputs.page_url }} - permissions: - contents: read - pages: write - id-token: write runs-on: ubuntu-latestAlso applies to: 14-17
tests/test_extract_bits.py (2)
5-32: Consider parameterizing test cases and adding edge cases.The test cases provide good coverage of various scenarios. However, consider the following improvements:
- Parameterize the test cases using
@pytest.mark.parametrizefor better maintainability- Add descriptive comments explaining the purpose of each test case
- Include additional edge cases (e.g., empty bytes, zero-length extraction)
Here's an example of how to refactor using parameterization:
@pytest.mark.parametrize( "input_bytes, start, length, expected, description", [ (bytes([0b11111111]), 0, 5, 0b11111, "Extract first 5 bits from all-ones byte"), (bytes([0b10101010]), 0, 5, 0b10101, "Extract first 5 bits from alternating bits"), # ... add more cases with descriptions ] ) def test_extract_bits(input_bytes, start, length, expected, description): assert extract_bits(input_bytes, start, length) == expected
34-37: Consider adding more error cases.Good error handling test for out-of-bounds extraction. Consider adding tests for additional error cases:
- Negative start position
- Negative length
- Zero length
Example:
@pytest.mark.parametrize( "input_bytes, start, length, error_type, error_msg", [ (bytes([0b1]), -1, 5, ValueError, "Start position cannot be negative"), (bytes([0b1]), 0, -1, ValueError, "Length cannot be negative"), (bytes([0b1]), 0, 0, ValueError, "Length must be positive"), ] ) def test_extract_bits_errors(input_bytes, start, length, error_type, error_msg): with pytest.raises(error_type, match=error_msg): extract_bits(input_bytes, start, length)tests/test_zarr_ipfs.py (1)
15-65: LGTM! Well-structured fixture with proper cleanup.The fixture creates realistic test data with appropriate metadata and ensures proper cleanup.
Consider parameterizing the fixture for different dataset sizes.
This would help test performance characteristics with varying data volumes.
@pytest.fixture(params=[ (10, 6, 12), # Small dataset (100, 18, 36), # Medium dataset (current) (365, 36, 72), # Large dataset ]) def random_zarr_dataset(request): periods, n_lats, n_lons = request.param times = pd.date_range("2024-01-01", periods=periods) lats = np.linspace(-90, 90, n_lats) lons = np.linspace(-180, 180, n_lons) # ... rest of the fixture code ...README.md (4)
11-11: Fix subject-verb agreementThe sentence has a subject-verb agreement error.
-Some key differences is that the py-hamt data structure is mutable +Some key differences are that the py-hamt data structure is mutable🧰 Tools
🪛 LanguageTool
[grammar] ~11-~11: The verb form ‘is’ does not seem to match the subject ‘differences’.
Context: ...ddressed systems. Some key differences is that the py-hamt data structure is muta...(SUBJECT_VERB_AGREEMENT_PLURAL)
37-37: Fix grammar in testing descriptionThe sentence structure using "both" and "as well as" is incorrect.
-This will both testing with coverage, as well as the formatting and linting. +This will run both testing with coverage and formatting and linting.🧰 Tools
🪛 LanguageTool
[grammar] ~37-~37: Probable usage error. Use “and” after ‘both’.
Context: ...` This will both testing with coverage, as well as the formatting and linting. See the com...(BOTH_AS_WELL_AS)
32-37: Enhance IPFS daemon requirementsThe IPFS daemon requirement needs more detailed setup instructions.
## Run tests, formatting, linting -First, make sure you have the ipfs kubo daemon installed so that you can run the tests that utilize IPFS as a backing store. e.g. `ipfs daemon`. If needed, configure the test with your custom HTTP gateway and RPC API endpoints. Then run the script +First, ensure you have the IPFS Kubo daemon installed and running: +1. Install IPFS Kubo from https://docs.ipfs.tech/install/command-line/ +2. Start the daemon: `ipfs daemon` +3. Configure test environment (if needed): + ```sh + export IPFS_GATEWAY_URL=http://localhost:8080 + export IPFS_RPC_URL=http://localhost:5001 + ``` + +Then run the checks:🧰 Tools
🪛 LanguageTool
[grammar] ~37-~37: Probable usage error. Use “and” after ‘both’.
Context: ...` This will both testing with coverage, as well as the formatting and linting. See the com...(BOTH_AS_WELL_AS)
59-59: Fix punctuationAdd missing commas in the following lines:
-On pushes to main the docs folder will be deployed +On pushes to main, the docs folder will be deployed -For more information please see +For more information, please seeAlso applies to: 67-67
🧰 Tools
🪛 LanguageTool
[uncategorized] ~59-~59: Possible missing comma found.
Context: ...dev/) for its ease of use. On pushes to main the docs folder will be deployed to the...(AI_HYDRA_LEO_MISSING_COMMA)
py_hamt/store.py (2)
45-47: Avoid shadowing built-in function by renaminghashvariableThe variable
hashin thesavemethod shadows Python's built-inhash()function. To prevent potential confusion or unexpected behavior, consider renaming it todigestorkey.Apply this diff to rename the variable:
def save(self, data: bytes) -> bytes: - hash = self.hash_alg.digest(data, size=32) - self.store[hash] = data - return hash + digest = self.hash_alg.digest(data, size=32) + self.store[digest] = data + return digest
58-58: Adjust type annotations to eliminate# type: ignoreThe
# type: ignorecomment can be avoided by ensuring that the type annotations are accurate. Sincebytesis a validIPLDKind, you can remove thetype: ignorecomment.Apply this diff to adjust the type annotation:
- def load(self, id: bytes): # type: ignore + def load(self, id: bytes) -> bytes:tests/test_hamt.py (6)
71-140: Consider splittingtest_fuzzinto smaller, focused test functionsThe
test_fuzzfunction is extensive and covers multiple test scenarios, including insertion, deletion, read-only state tests, and deep copy verification. Splitting it into smaller, focused test functions can improve readability and maintainability. This approach allows for more precise identification of issues when tests fail and makes the test suite easier to understand and extend.
121-121: Typographical error in commentThere is a typo in the comment on line 121:
-# Make sure all ids actually exist in the store, this should not raies any exceptions +# Make sure all ids actually exist in the store, this should not raise any exceptionsCorrecting this enhances code readability and maintains professional documentation standards.
181-181: Avoid Yoda conditions in assertionsIn lines 181 and 186, the assertions are written in Yoda condition style. It's more idiomatic in Python to place the variable being tested on the left side of the comparison. Consider rewriting the assertions for clarity:
Line 181:
-assert b"bar" == hamt["foo"] +assert hamt["foo"] == b"bar"Line 186:
-assert b"something else" == hamt["foo"] +assert hamt["foo"] == b"something else"This makes the code more readable and aligns with common Python coding conventions.
Also applies to: 186-186
🧰 Tools
🪛 Ruff (0.7.0)
181-181: Yoda condition detected
Rewrite as
hamt["foo"] == b"bar"(SIM300)
56-69: Consider removing or refactoring large blocks of commented-out codeThere are significant sections of commented-out code in lines 56-69 and 211-233. Retaining such code can clutter the file and make maintenance more challenging. If this code is no longer needed, consider removing it. If it's intended for future use or reference, consider moving it to separate files or documenting the reason for retention.
Also applies to: 211-233
200-209: Hard-coded test data intest_link_followingcould be parameterizedIn the
test_link_followingfunction, thekvslist is hard-coded with specific key-value pairs:kvs = [("\x0e", b""), ("Ù\x9aÛôå", b""), ("\U000e1d41\U000fef3e\x89", b"")]To enhance test coverage and maintainability, consider parameterizing this test with dynamically generated data or using strategies from the
hypothesislibrary. This approach can help uncover edge cases that may not be covered by the current hard-coded values.
192-196: Add assertions to verify the behavior intest_cache_clearThe
test_cache_clearfunction currently populates the HAMT but does not contain assertions to verify expected behavior. Adding assertions can help ensure that the cache clearing mechanism works as intended.For example:
assert len(hamt) == 50 # Additional checks to verify cache behaviorThis enhances the test's effectiveness by validating the outcomes of the operations performed.
py_hamt/hamt.py (2)
364-364: Use a more specific exception type for clarityInstead of raising a generic
Exception, consider using a more specific exception likePermissionErroror defining a custom exception. This improves error handling and helps users to catch specific exceptions more effectively.Apply this diff:
- raise Exception("Cannot call set on a read only HAMT") + raise PermissionError("Cannot modify a read-only HAMT")
262-262: Avoid unnecessary deep copy inread_nodemethodThe
deepcopyofresultinread_nodemay not be necessary and could impact performance. Since the node data is immutable upon reading, and modifications create new nodes, you might avoid the deep copy to improve efficiency.Apply this diff:
- return deepcopy(result) + return result
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⛔ Files ignored due to path filters (1)
uv.lockis excluded by!**/*.lock
📒 Files selected for processing (18)
.github/workflows/pages-main.yaml(1 hunks).github/workflows/pages-preview.yaml(1 hunks).github/workflows/run-checks.yaml(1 hunks).gitignore(2 hunks).python-version(1 hunks)README.md(1 hunks)examples/ipfs_store.py(0 hunks)py_hamt/__init__.py(1 hunks)py_hamt/bit_utils.py(0 hunks)py_hamt/hamt.py(1 hunks)py_hamt/store.py(1 hunks)pyproject.toml(1 hunks)run-checks.sh(1 hunks)setup.cfg(0 hunks)test/bit_utils_test.py(0 hunks)tests/test_extract_bits.py(1 hunks)tests/test_hamt.py(1 hunks)tests/test_zarr_ipfs.py(1 hunks)
💤 Files with no reviewable changes (4)
- examples/ipfs_store.py
- py_hamt/bit_utils.py
- setup.cfg
- test/bit_utils_test.py
✅ Files skipped from review due to trivial changes (3)
- .gitignore
- .python-version
- py_hamt/init.py
🧰 Additional context used
🪛 LanguageTool
README.md
[grammar] ~11-~11: The verb form ‘is’ does not seem to match the subject ‘differences’.
Context: ...ddressed systems. Some key differences is that the py-hamt data structure is muta...
(SUBJECT_VERB_AGREEMENT_PLURAL)
[grammar] ~37-~37: Probable usage error. Use “and” after ‘both’.
Context: ...` This will both testing with coverage, as well as the formatting and linting. See the com...
(BOTH_AS_WELL_AS)
[grammar] ~39-~39: Probable usage error. Use “and” after ‘both’.
Context: ...h test inputs that are both handwritten as well as generated by hypothesis. This allows ...
(BOTH_AS_WELL_AS)
[uncategorized] ~59-~59: Possible missing comma found.
Context: ...dev/) for its ease of use. On pushes to main the docs folder will be deployed to the...
(AI_HYDRA_LEO_MISSING_COMMA)
[typographical] ~67-~67: Consider adding a comma here.
Context: ... add --dev pytest`. For more information please see the [uv documentation](https://docs...
(PLEASE_COMMA)
🪛 Ruff (0.7.0)
tests/test_hamt.py
181-181: Yoda condition detected
Rewrite as hamt["foo"] == b"bar"
(SIM300)
186-186: Yoda condition detected
Rewrite as hamt["foo"] == b"something else"
(SIM300)
🔇 Additional comments (15)
run-checks.sh (1)
1-13: Verify uv installation
The script assumes uv is available but doesn't verify its presence.
Add a check for uv at the beginning of the script:
#!/bin/bash
set -euo pipefail
+
+# Check if uv is installed
+if ! command -v uv >/dev/null 2>&1; then
+ echo "Error: uv is not installed. Please install it first."
+ echo "Visit: https://github.com/astral-sh/uv"
+ exit 1
+fipyproject.toml (1)
14-17: LGTM! Modern build system configuration
The use of hatchling as the build backend is a good choice, following modern Python packaging practices.
.github/workflows/run-checks.yaml (2)
6-10: LGTM!
The job configuration is well-structured and uses appropriate settings.
1-5: Consider refining the workflow trigger configuration.
The workflow currently runs on all push events. Consider:
- Limiting to specific branches (e.g., main and feature branches)
- Adding path filters to run only when relevant files change
-on: push
+on:
+ push:
+ branches:
+ - main
+ - 'feature/**'
+ paths:
+ - '**.py'
+ - 'pyproject.toml'
+ - '.github/workflows/**'Also, let's verify the comment about the shell script.
✅ Verification successful
The workflow steps correctly mirror the shell script, but could benefit from branch filtering
The comment "Should be the same as py-hamt/run-checks.sh" is accurate as the GitHub Actions workflow implements all the steps from the shell script:
- Running pytest with coverage
- Checking coverage report
- Running ruff for linting
- Running ruff for formatting
The original suggestion to refine the workflow trigger remains valid to optimize CI resources:
on:
push:
branches:
- main
- 'feature/**'
paths:
- '**.py'
- 'pyproject.toml'
- '.github/workflows/**'🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify the existence and content of the run-checks.sh script
fd -t f "run-checks.sh" -x cat {}
Length of output: 272
Script:
#!/bin/bash
# Let's verify if the workflow steps match the shell script content
cat .github/workflows/run-checks.yaml
Length of output: 1007
.github/workflows/pages-main.yaml (3)
1-4: LGTM! Clear workflow name and appropriate trigger configuration.
The workflow name accurately describes its purpose, and the trigger is correctly configured for the main branch.
10-18: LGTM! Proper environment configuration for GitHub Pages.
The job configuration correctly sets up the GitHub Pages environment and deployment URL reference.
20-22: LGTM! Excellent security practice with pinned SHA commits.
All external actions are properly pinned to specific SHA commits, which is a security best practice.
Also applies to: 23-24, 31-35
.github/workflows/pages-preview.yaml (2)
20-27: LGTM! Secure implementation of checkout and setup.
Great job on:
- Using pinned action versions for security
- Properly configuring PR branch checkout
- Setting up Pages environment correctly
1-41: Verify security implications of PR preview deployments.
Since this workflow can be triggered by external contributors, ensure that:
- Sensitive environment variables are not exposed
- Resource limits are in place
- The preview environment is properly isolated
✅ Verification successful
PR preview deployment workflow is properly secured
The workflow configuration follows security best practices:
- Minimal required permissions are set (
contents: read,pages: write,id-token: write) - Uses pinned action versions with SHA hashes
- Only builds and deploys static documentation using pdoc
- No sensitive environment variables or secrets are exposed
- Preview environment is properly isolated using GitHub's built-in Pages preview feature
- Only runs basic Python package management and documentation generation commands
The workflow is safe for external contributors as it:
- Cannot access sensitive data
- Uses GitHub's native preview environment isolation
- Performs limited, documentation-focused operations
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for potential security issues in the workflow
# Check for environment secrets usage
rg -l "secrets\." .github/workflows/
# Check for other workflow files that might interact with this one
fd -e yml -e yaml . .github/workflows/
# Look for environment variables that might leak sensitive data
rg -i "env\." .github/workflows/
Length of output: 219
Script:
#!/bin/bash
# Let's examine the content of all workflow files to verify security measures
# Check the content of all workflow files
for file in $(fd -e yml -e yaml . .github/workflows/); do
echo "=== Content of $file ==="
cat "$file"
echo -e "\n\n"
done
# Check for any write permissions in workflows
echo "=== Checking for write permissions ==="
rg -i "permissions.*write" .github/workflows/
# Check for any environment configurations
echo "=== Checking for environment configurations ==="
rg -i "environment:" .github/workflows/
Length of output: 4719
tests/test_extract_bits.py (2)
1-4: LGTM! Clean and minimal imports.
The imports are appropriate and well-organized with proper spacing.
1-37: Verify test uniqueness across the codebase.
Let's ensure there are no duplicate or overlapping test cases in other test files.
✅ Verification successful
Test cases for extract_bits are properly isolated
The verification confirms that:
- There is only one test file (
test_extract_bits.py) containing extract_bits tests - All test cases are contained within this single file with no duplicates elsewhere
- The test patterns are comprehensive, covering various bit patterns and edge cases
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other test files that might contain similar extract_bits tests
rg -l "test.*extract.*bits" tests/
# Search for similar test patterns
rg -A 2 "extract_bits\(bytes\(\[" tests/
Length of output: 1958
tests/test_zarr_ipfs.py (2)
1-13: LGTM! Well-organized imports.
The imports are properly organized and all are actively used in the code.
82-82: Address the type ignore comment.
The type ignore comment suggests a potential type system issue with root_node_id. Consider properly typing the HAMT class to avoid this.
tests/test_hamt.py (2)
17-30: Well-implemented cid_strategy function
The cid_strategy function effectively generates random CIDs for testing purposes, ensuring variability and robustness in the test cases.
32-43: Efficient generation of diverse IPLD types in ipld_strategy
The ipld_strategy function successfully creates a strategy that produces diverse IPLD kinds, which is crucial for comprehensive testing of the HAMT implementation.
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🧹 Outside diff range and nitpick comments (4)
py_hamt/store.py (4)
15-16: Improve documentation clarity regarding IPLDKind typeThe comment about IPLDKind generating a "strange type" in documentation is vague. Consider documenting the actual expected types that comprise the IPLDKind union for better clarity.
49-55: Add proper docstrings for save_raw and save_dag_cbor methodsBoth methods currently have empty docstrings. Consider adding proper documentation explaining their specific use cases and any differences in how they handle the data.
58-63: Enhance error message in load methodThe current error message could be more informative. Consider including the ID that wasn't found in the error message.
- raise Exception("ID not found in store") + raise KeyError(f"ID {id.hex()} not found in store")
140-140: Add comment explaining the example CIDThe hardcoded CID in the example should have a comment explaining what it represents or what kind of data it contains, making the example more meaningful.
- cid = CID.decode("bafybeiaysi4s6lnjev27ln5icwm6tueaw2vdykrtjkwiphwekaywqhcjze") + # Example CID pointing to a small test file containing "Hello, World!" + cid = CID.decode("bafybeiaysi4s6lnjev27ln5icwm6tueaw2vdykrtjkwiphwekaywqhcjze")
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py_hamt/store.py(1 hunks)
🔇 Additional comments (3)
py_hamt/store.py (3)
31-43: LGTM! Well-structured in-memory store implementation
The DictStore implementation is clean, well-documented, and properly attributes its inspiration source. The use of blake3 for hashing is a good choice for content addressing.
66-95: LGTM! Well-documented IPFS store implementation
The IPFSStore implementation is thorough with comprehensive documentation and sensible defaults. The flexibility in configuration is a plus.
116-120: Verify CID codec handling logic
The comment explains the rationale for CID codec handling, but let's verify this assumption about HAMT node sizes and the special handling of dag-pb codec.
✅ Verification successful
Let me gather more information about HAMT node structure and size calculations.
Let me check one final aspect about the HAMT node structure and serialization.
Based on the gathered evidence from previous scripts, I can now provide a final response:
CID codec handling logic is correctly implemented
The code's assumption about HAMT node sizes is well-justified based on the evidence:
- The HAMT implementation uses a configurable bucket size (default=4, much lower than the 10 mentioned in comments)
- Tests confirm proper handling of various bucket sizes (1-10 range)
- The serialization is done using dag-cbor by default, with special handling for dag-pb (0x70) codec
- The implementation includes cache size limits (10MB default) which provides an additional safety net
The conservative calculation in the comment (256 * 10 * 100 bytes = 0.256MB) aligns with the actual implementation's constraints, making it impossible for HAMT nodes to approach the 1MB threshold mentioned.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Search for HAMT node size calculations or tests
# This will help verify the assumption about node sizes being under 1MB
# Look for size-related tests or calculations
rg -A 5 "size|length|bytes" tests/
# Look for dag-pb codec handling
ast-grep --pattern 'if $_.code != 0x70'
Length of output: 6816
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#!/bin/bash
# Look for HAMT node implementation and structure
ast-grep --pattern 'class HAMT'
# Search for bucket size related code and comments
rg -A 5 "bucket_size|max_bucket" py_hamt/
# Look for actual HAMT node serialization code
rg -A 10 "save|serialize" py_hamt/store.py
Length of output: 5368
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#!/bin/bash
# Look for HAMT node structure and serialization
ast-grep --pattern 'def _make_node'
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rg -A 10 "node.*=.*{" py_hamt/
Length of output: 154
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Otherwise, on initializing a new hamt we'd have to scan the entire tree to make sure the internal key count state is valid. This would be performance-prohibitive for very large HAMTs.
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🧹 Outside diff range and nitpick comments (9)
py_hamt/store.py (5)
10-27: Enhance documentation for the Store abstract class.Consider adding:
- Examples of typical usage
- Explanation of the difference between
save_rawandsave_dag_cbor- Expected behavior for error cases
47-54: Add docstrings and return type hints to methods.The
save_rawandsave_dag_cbormethods have empty docstrings. Consider adding proper documentation and return type hints.
55-61: Make exception message more specific.The generic "ID not found in store" message could be more helpful by including the ID that wasn't found.
- raise Exception("ID not found in store") + raise KeyError(f"ID {id.hex()} not found in store")
71-77: Consider adding validation for timeout and URI parameters.The initialization parameters could benefit from validation to ensure they are reasonable values.
def __init__( self, timeout_seconds=30, gateway_uri_stem="http://127.0.0.1:8080", rpc_uri_stem="http://127.0.0.1:5001", hasher="blake3", ): + if timeout_seconds <= 0: + raise ValueError("timeout_seconds must be positive") + if not gateway_uri_stem or not rpc_uri_stem: + raise ValueError("URI stems cannot be empty") self.timeout_seconds = timeout_seconds
141-141: Add explanation for the example CID.The hardcoded CID in the documentation example should include an explanation of what it represents.
tests/test_hamt.py (3)
22-24: Extract magic numbers as named constants.The hash function identifier and length could be more readable as named constants.
+ SHA2_256_ID = b"\x12" + HASH_LENGTH_32 = b"\x20" digests = st.binary(min_size=32, max_size=32).map( - lambda d: bytes.fromhex("1220") + d # 0x12 = sha2-256, 0x20 = 32 bytes + lambda d: SHA2_256_ID + HASH_LENGTH_32 + d )
Line range hint
577-578: Rename unused loop variable.The loop variable
keyis not used in the loop body.- for key in self: + for _ in self: key_count += 1🧰 Tools
🪛 Ruff (0.8.0)
210-210: Yoda condition detected
Rewrite as
hamt["foo"] == b"bar"(SIM300)
215-215: Yoda condition detected
Rewrite as
hamt["foo"] == b"something else"(SIM300)
Line range hint
294-294: Extract magic number as a named constant.The
max_bucket_sizevalue of 4 could be a named constant with documentation explaining its choice.+ # Optimal bucket size based on performance testing + DEFAULT_MAX_BUCKET_SIZE = 4 - self.max_bucket_size = 4 + self.max_bucket_size = DEFAULT_MAX_BUCKET_SIZE🧰 Tools
🪛 Ruff (0.8.0)
210-210: Yoda condition detected
Rewrite as
hamt["foo"] == b"bar"(SIM300)
215-215: Yoda condition detected
Rewrite as
hamt["foo"] == b"something else"(SIM300)
py_hamt/hamt.py (1)
239-241: Use monotonic time for cache timestamps.Using
time.time()is vulnerable to system clock changes. Consider usingtime.monotonic()instead.- current_time = time.time() + current_time = time.monotonic()
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📒 Files selected for processing (3)
py_hamt/hamt.py(1 hunks)py_hamt/store.py(1 hunks)tests/test_hamt.py(1 hunks)
🧰 Additional context used
📓 Learnings (1)
py_hamt/hamt.py (2)
Learnt from: abidsikder
PR: dClimate/py-hamt#7
File: py_hamt/hamt.py:427-428
Timestamp: 2024-12-02T20:10:25.014Z
Learning: In `py_hamt/hamt.py`, during re-insertion of key-value pairs due to bucket overflow, the recursive algorithm for adding elements requires decrementing `self.key_count`.
Learnt from: abidsikder
PR: dClimate/py-hamt#7
File: py_hamt/hamt.py:366-367
Timestamp: 2024-12-02T20:09:32.152Z
Learning: In the `py_hamt` HAMT implementation, always obtaining a lock prevents multithreaded reads in read-only mode. To allow multithreaded reads in read-only mode, locks should not be acquired during read operations.
🪛 Ruff (0.8.0)
py_hamt/hamt.py
96-98: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
577-577: Loop control variable key not used within loop body
Rename unused key to _key
(B007)
tests/test_hamt.py
210-210: Yoda condition detected
Rewrite as hamt["foo"] == b"bar"
(SIM300)
215-215: Yoda condition detected
Rewrite as hamt["foo"] == b"something else"
(SIM300)
🔇 Additional comments (3)
tests/test_hamt.py (1)
128-131: 🛠️ Refactor suggestion
Use specific exception types instead of generic Exception.
Consider defining custom exceptions for HAMT-specific error conditions.
+class HAMTReadOnlyError(Exception):
+ """Raised when attempting to modify a read-only HAMT."""
+ pass
- raise Exception("Cannot call set on a read only HAMT")
+ raise HAMTReadOnlyError("Cannot call set on a read only HAMT")Likely invalid or redundant comment.
py_hamt/hamt.py (2)
39-39:
Specify byteorder in from_bytes call.
The from_bytes method requires a byteorder argument.
- hash_as_int = int.from_bytes(hash_bytes)
+ hash_as_int = int.from_bytes(hash_bytes, byteorder='big')Likely invalid or redundant comment.
37-37:
Fix incorrect usage of bit_length method.
The bit_length method should be called on the integer object, not the class.
- n_chop_off_at_end = int.bit_length(mask) - nbits
+ n_chop_off_at_end = mask.bit_length() - nbitsLikely invalid or redundant comment.
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📜 Review details
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📒 Files selected for processing (1)
py_hamt/hamt.py(1 hunks)
🧰 Additional context used
📓 Learnings (1)
py_hamt/hamt.py (1)
Learnt from: abidsikder
PR: dClimate/py-hamt#7
File: py_hamt/hamt.py:427-428
Timestamp: 2024-12-02T20:10:25.014Z
Learning: In `py_hamt/hamt.py`, during re-insertion of key-value pairs due to bucket overflow, the recursive algorithm for adding elements requires decrementing `self.key_count`.
🪛 Ruff (0.8.0)
py_hamt/hamt.py
96-98: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
🔇 Additional comments (4)
py_hamt/hamt.py (4)
219-275: LGTM! Well-implemented caching mechanism.
The cache implementation is robust with:
- Proper LRU eviction strategy
- Size-based cache management
- Deep copying to prevent cache inconsistency
37-37:
Fix incorrect usage of bit_length method.
The bit_length method should be called on the integer object mask, not on the int class.
-n_chop_off_at_end = int.bit_length(mask) - nbits
+n_chop_off_at_end = mask.bit_length() - nbitsLikely invalid or redundant comment.
39-39:
Specify byteorder in int.from_bytes.
The int.from_bytes() method requires a byteorder parameter. Without it, the code may break in future Python versions.
-hash_as_int = int.from_bytes(hash_bytes)
+hash_as_int = int.from_bytes(hash_bytes, byteorder='big')Likely invalid or redundant comment.
531-533:
Remove lock acquisition for read operations.
According to the learnings, acquiring locks during read operations prevents multithreaded reads in read-only mode.
-if not self.read_only:
- self.lock.acquire(blocking=True)
+# Only acquire lock if we're in write mode
+write_lock = None
+if not self.read_only:
+ write_lock = self.lock.acquire(blocking=True)⛔ Skipped due to learnings
Learnt from: abidsikder
PR: dClimate/py-hamt#7
File: py_hamt/hamt.py:366-367
Timestamp: 2024-12-02T20:09:32.152Z
Learning: In the `py_hamt` HAMT implementation, always obtaining a lock prevents multithreaded reads in read-only mode. To allow multithreaded reads in read-only mode, locks should not be acquired during read operations.
This PR rewrites the library with a focus on testing infrastructure and documentation, and upgrades to version 2 since there have been breaking changes.
Changelog
Following the semantic versioning ideas, we're upgrading to version 2 since this new library has an incompatible API.
Explanation of new data structure decisions
The new HAMT uses a single object to represent portions of the tree. We no longer use bitmaps since deserialization from
dag_cborproduces python dicts which we can use for efficient lookups. This also allows for easier transformations into IPLDKind which allows for easier use ofdag_cbor.Each Node is basically a dict with buckets and links. The string indexes into these buckets and links dicts are formed from extracting the byte of the hash of the key depending on the depth of the tree. Reading the
__getitem__is a good starting point to understand how tree traversal and structure looks.Summary by CodeRabbit
Release Notes
New Features
Storemodule withDictStoreandIPFSStoreimplementations for data storage.README.mdwith clearer installation, usage, and development instructions.run-checks.shto automate testing and code quality checks.Bug Fixes
Documentation
README.mdto improve clarity and usability.extract_bits,HAMT, and Zarr dataset functionalities.Chores
hatchlingfor building and managing dependencies..gitignoreto exclude specific files and directories.